ABSTRACT
This study is a comparative text analysis of Al Jazeera English, BBC News, and CNN on the Coronavirus pandemic. Only the text-based news from April 13 to April 20, 2020, were collected from the official Facebook pages of the respective news organizations. Based on the framing theory, the computer-based text analysis using MAXQDA software was used to conduct the research. The study found how these internationally recognized media outlets frame their news using word frequency, the combination of multiple words, and semantic relationships among the news published on their Facebook pages. A total of 105 news were selected out of 185 and 89,465 words were analyzed to observe how they framed the Novel Coronavirus crisis. Six individual frames were found and the results revealed four similarities and two differences among the frames. The similarities and differences were discussed based on different approaches to framing including proximity and political agendas.
ABSTRACT
BACKGROUND: As COVID-19 spreads worldwide, an infodemic - i.e., an over-abundance of information, reliable or not - spreads across the physical and the digital worlds, triggering behavioral responses which cause public health concern. METHODS: We study 200 million interactions captured from Twitter during the early stage of the pandemic, from January to April 2020, to understand its socio-informational structure on a global scale. FINDINGS: The COVID-19 global communication network is characterized by knowledge groups, hierarchically organized in sub-groups with well-defined geo-political and ideological characteristics. Communication is mostly segregated within groups and driven by a small number of subjects: 0.1% of users account for up to 45% and 10% of activities and news shared, respectively, centralizing the information flow. INTERPRETATION: Contradicting the idea that digital social media favor active participation and co-creation of online content, our results imply that public health policy strategies to counter the effects of the infodemic must not only focus on information content, but also on the social articulation of its diffusion mechanisms, as a given community tends to be relatively impermeable to news generated by non-aligned sources.